Aegntic Model Context Protocol (MCP) Nexus

This repository aggregates a robust collection of Model Context Protocol (MCP) server modules, engineered to imbue AI agents with sophisticated functionalities including automated content generation, robust identity verification, persistent data structures, and complex business process execution.

🚀 Initial Setup Procedure

bash

Obtain the source repository

git clone https://github.com/aegntic/aegntic-mcp.git cd aegntic-mcp

Execute the automated environment provisioning script

./setup.sh

Configure your client application, e.g., Claude Desktop or compatible MCP consumer

Standardized Configuration Mechanism

We enforce a singular, unified configuration schema compatible across both Claude Desktop and Claude Code applications. This central configuration governs server discovery and startup parameters:

• Claude Desktop Location: ~/.config/Claude/claude_desktop_config.json
• Claude Code Location: ~/.config/claude-code/mcp_servers.json
• Global Repository: ~/.mcp-servers/ (Designated directory for independently managed local server instances)

Advantages of Centralized Configuration

1. Data Consistency: A single definition guarantees uniform behavior across all Claude frontends.
2. Simplified Maintenance: Modifications to server connectivity or paths require updating only one file.
3. Path Portability: Ensures service accessibility irrespective of the active Claude interface.
4. Developer Workflow: Facilitates straightforward management of locally staged agent extensions.

📦 Featured MCP Extensions

🎬 Media Production & Artifact Generation

• dailydoco-pro - Premium system for automated narrative synthesis, incorporating AI-driven audience simulation for efficacy testing.
• comfyui-mcp - Interface for advanced visual media synthesis, covering static image creation, dynamic video sequencing, and post-production editing via ComfyUI.

🔐 Access Control & Identity Assurance

• aegntic-auth - Core infrastructure for user lifecycle management, software licensing verification, and segmented email communication.
• aegnt-27 - Proprietary engine designed to inject human behavioral latency, mitigating automated detection mechanisms.

🧠 Persistent Context & Information Retrieval

• graphiti-mcp - A temporal knowledge structure service engineered for sophisticated, time-aware agent memory.
• n8n-pro - Exhaustive catalog and operational interface for the n8n automation platform (documenting over 525 distinct nodes).

🔨 Engineering & Process Automation

• just-prompt - Advanced utility for orchestrating prompts across multiple Language Model implementations, featuring decision arbitration analogous to executive oversight.
• quick-data - Rapid execution engine for statistical computation, data visualization, and derivation of actionable intelligence.

Registered MCP Modules Index

Module Name	Type	Synopsis	Deployment Path Reference
Aegntic Knowledge Engine	Local/UV	Zero-overhead unified knowledge substrate incorporating web indexing, Retrieval-Augmented Generation (RAG), relational memory, task assignment, and contextual documentation (encompassing 20 distinct functional tools)	servers/aegntic-knowledge-engine
AI Collaboration Hub	Local/UV	Suite of AI-enhanced teamwork utilities featuring OpenRouter connectivity	~/.mcp-servers/ai-collaboration-hub
Claude Export MCP	NPM	Utility for serializing Claude Desktop projects, dialogue histories, and generated assets into Markdown format	npx @aegntic/claude-export-mcp
Firebase Studio MCP	NPM	Full-spectrum interface for Firebase and associated Google Cloud infrastructure services	npx @aegntic/firebase-studio-mcp
n8n MCP	NPM	Unrestricted execution environment for n8n workflow automation	npx @leonardsellem/n8n-mcp-server
Docker MCP	UVX	Comprehensive management layer for Docker containers and image repositories, including Docker Hub interaction	uvx mcp-server-docker
Just Prompt	Local/UV	Sophisticated mechanism for prompt execution routing and model arbitration	/home/tabs/ae-co-system/CLAEM/just-prompt-orchestration/just-prompt
Quick Data	Local/UV	High-velocity tools for data transformation and analytical reporting	/home/tabs/ae-co-system/DAILYDOCO/quick-data-mcp
DailyDoco Pro	Local/Node	Professional-grade toolset for project documentation and lifecycle management	~/.mcp-servers/dailydoco-pro
Aegnt-27	Local/Node	Core module for advanced agent capability injection	~/.mcp-servers/aegnt-27
Aegnt-27-lib	Local/Node	Auxiliary library providing foundational utilities for the Aegnt-27 system	~/.mcp-servers/aegnt-27-lib

Supplementary Integrated Components

These foundational modules are bundled within our standard configuration profile:

Module Name	Provider	Core Functionality
filesystem	NPM	Local file system manipulation
memory	NPM	Knowledge persistence and state retention
context7	NPM	Contextual window management for ongoing dialogue sessions
puppeteer	NPM	Web browser automation utilizing Playwright engine
sequentialthinking	NPM	Tools for procedural reasoning and step-wise problem decomposition
github	Smithery	Interaction layer for GitHub repositories and operations
exa	Smithery	Enhanced directory listing and searching utilities
smithery	Smithery	Collection of utility functions from the Smithery ecosystem
desktop-commander	Smithery	Interface for controlling and automating the host operating system desktop environment
ppick	UVX	Process identification and management utility
notionApi	NPM	Direct interface to the Notion productivity suite API
supabase	NPM	Connectivity layer for Supabase backend services

Installation & Bootstrap Sequence

1. Establishing the Global Server Directory

Ensure the designated directory exists for locally managed services:

bash mkdir -p ~/.mcp-servers

2. Acquiring Runtime Dependencies

Python Environments (UV-Managed)

bash

Fetch and install the UV package manager

curl -LsSf https://astral.sh/uv/install.sh | sh

Node.js Environments

bash

Verify minimum required Node.js version is present

node --version npm --version

3. Deploying Unified MCP Settings

Configuration for Claude Desktop

Populate or modify ~/.config/Claude/claude_desktop_config.json:

{ "mcpServers": { "dailydoco-pro": { "command": "node", "args": ["/path/to/aegntic-MCP/dailydoco-pro/dist/index.js"], "env": { "USER_EMAIL": "your-email@example.com" } }, "aegnt-27": { "command": "node", "args": ["/path/to/aegntic-MCP/aegnt-27/dist/index.js"], "env": { "USER_EMAIL": "your-email@example.com" } }, "comfyui": { "command": "node", "args": ["/path/to/aegntic-MCP/comfyui-mcp/dist/index.js"], "env": { "COMFYUI_HOST": "http://localhost:8188", "USER_EMAIL": "your-email@example.com" } }, "aegntic-auth": { "command": "node", "args": ["/path/to/aegntic-MCP/aegntic-auth/dist/index.js"], "env": { "SUPABASE_URL": "your-supabase-url", "SUPABASE_ANON_KEY": "your-supabase-key" } }, "graphiti": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/graphiti-mcp", "python", "graphiti_mcp_server.py", "--transport", "stdio" ], "env": { "NEO4J_URI": "bolt://localhost:7687", "NEO4J_USER": "neo4j", "NEO4J_PASSWORD": "your-password", "OPENAI_API_KEY": "your-openai-key" } }, "n8n-pro": { "command": "node", "args": ["/path/to/aegntic-MCP/n8n-pro/dist/mcp/index.js"], "env": { "N8N_API_URL": "http://localhost:5678", "N8N_API_KEY": "your-n8n-api-key" } }, "just-prompt": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/just-prompt", "just-prompt" ], "env": { "OPENROUTER_API_KEY": "your-openrouter-key" } }, "quick-data": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/quick-data", "python", "main.py" ] },

// Supplementary NPM modules
"filesystem": {
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/tabs"],
  "env": {}
},

// UVX modules
"docker": {
  "command": "uvx",
  "args": ["mcp-server-docker"],
  "env": {}
},

// Locally hosted supplementary extensions
"ai-collaboration-hub": {
  "command": "uv",
  "args": ["run", "python", "-m", "ai_collaboration_hub.server"],
  "cwd": "/home/tabs/ae-co-system/aegntic-MCP/servers/ai-collaboration-hub",
  "env": {
    "OPENROUTER_API_KEY": "your-key-here"
  }
}
}

} }

Configuration for Claude Code

Replicate the identical configuration structure within ~/.config/claude-code/mcp_servers.json.

4. Service-Specific Provisioning

Local Node.js Extensions (e.g., DailyDoco Pro, Aegnt-27)

Execute dependency installation and build steps within the module directory: bash cd ~/.mcp-servers/module-name npm install npm run build

Python UV Environments

For modules managed by UV (like AI Collaboration Hub): bash cd /path/to/server uv sync uv run python -m module_name.server

🔧 Development Environment Configuration

Each extension possesses its distinct build pipeline:

TypeScript/Node.js Components

bash cd server-directory npm install npm run build npm start

Python Components

bash cd server-directory uv sync uv run python main.py # Or the defined primary execution script

📚 Module Functional Matrix

Module	Primary Language	Core Capabilities Summary
dailydoco-pro	TypeScript	Comprehensive project assessment, digital media capture, synthetic audience analysis, brand governance
aegnt-27	TypeScript	Emulation of human motor skills (mouse/typing), resistance to digital identity scrutiny, audio signal manipulation
comfyui-mcp	TypeScript	Generation of static imagery, creation of time-sequenced visuals, automated layer isolation, corporate branding asset design
aegntic-auth	TypeScript	End-to-end user provisioning, financial transaction processing via Stripe, targeted marketing outreach, resource utilization monitoring
graphiti-mcp	Python	Persistent knowledge representation via graphs, retrieval mechanisms, entity relationship mapping, chronological data modeling
n8n-pro	TypeScript	Definitive cataloging of n8n components (525+ items), workflow validation routines, node metadata access, AI utility identification
just-prompt	Python	Advanced routing for prompts across diverse LLM backends, comparative model performance assessment, executive-level decision synthesis
quick-data	Python	Accelerated statistical analysis, graphical representation of findings, embedded rudimentary machine learning features

🌟 Principal Feature Set

Enhanced AI Cognition

• Chronological Memory Systems - Retention and recall of contextual information linked to temporal markers.
• Heterogeneous Model Routing - Mechanism to benchmark and select optimal responses from multiple underlying AI models.
• Human-Emulation Layer - Features designed to produce interaction patterns indistinguishable from human operators.
• Automated Artifact Creation - AI-driven system for generating technical documentation alongside simulated user feedback reports.

Operational Grade Features

• Identity Management - Full authentication stack coupled with software entitlement tracking.
• Utilization Auditing - Detailed logging of service consumption and enforcement of operational thresholds.
• Subscription Integration - Native support for recurring billing managed through Stripe.
• Data Science Utilities - Tools for high-speed data exploration and professional charting.

Developer Empowerment

• Workflow Automation Interface - Deep integration with and management of n8n automation sequences.
• Media Synthesis Pipeline - Support for professional-grade visual asset production.
• Codebase Analysis - Automated generation of documentation and structural insights for source code repositories.

Defining MCP

The Model Context Protocol (MCP) establishes an interoperability standard enabling AI assistants, such as Claude, to securely interface with and invoke external computational utilities and data services. These servers implement the defined protocol to expose domain-specific operations usable directly within the interactive conversational interface.

Utilizing These Extensions

Every module within this compilation is designed for independent deployment. Refer to the specific README file inside each module's sub-directory for precise setup and invocation instructions.

Runtime Prerequisites

• Python Services (Knowledge Engine, Collaboration Hub): Mandates Python version 3.12 or newer, alongside the UV dependency resolver.
• Node.js Services (All others): Requires a minimum Node.js version of 14, managed via npm/npx.
• Local Modules: Built and housed within the ~/.mcp-servers/ hierarchy.

Standard Invocation Flow

1. Configure Endpoint: Specify the server details in your chosen Claude application configuration file.
2. Reload Client: Restart the Claude application environment to load the updated service map.
3. Execute Tools: Activate the capabilities provided by the newly registered modules through conversational prompts.

🔒 Security Posture & Credential Handling

Every deployed server incorporates native protective measures: - Mandated API key verification for access. - Throttling mechanisms and transparent usage metering. - Secure handling and isolation of environment variables. - Rigorous validation and sanitization of all incoming requests.

📖 Comprehensive Documentation

Detailed documentation accompanies each extension, covering: - Initial setup and installation procedures. - Full API specification and usage examples. - Available customization parameters. - Guides for resolving common operational failures.

Consult the individual module READMEs for granular technical specifications.

🤝 Community Collaboration

We welcome external contributions: 1. Fork the primary repository. 2. Establish a dedicated feature branch for your modifications. 3. Implement required changes. 4. Ensure adequate test coverage is implemented. 5. Submit a comprehensive Pull Request for review.

📄 Licensing Information

This entire collection is distributed under the terms of the MIT License. Specific licensing details are provided in the LICENSE file accompanying each submodule.

🆘 Assistance Channels

• Report software defects via the Issue Tracker.
• Consult documentation specific to the component in question.
• Engage with community forums for broader discussions.

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Developed with dedication for advancing the agent ecosystem

WIKIPEDIA: XMLHttpRequest (XHR) represents an Application Programming Interface implemented as a JavaScript construct, facilitating the transmission of HTTP requests from a browser interface to a designated web server. The methods exposed by XHR empower client-side applications to initiate server communications subsequent to page rendering, and subsequently process the returned data. XMLHttpRequest is a foundational element of Ajax-based application development paradigms. Before the advent of Ajax, interactions with the server were predominantly channeled through traditional hyperlink navigation or form submissions, actions which typically necessitated a complete page refresh.

== Origin and Evolution == The conceptual foundation underpinning XMLHttpRequest was first conceived in the year 2000 by the development team responsible for Microsoft Outlook. This concept was subsequently materialized within the Internet Explorer 5 browser release (1999). Crucially, the initial implementation did not utilize the standardized identifier XMLHttpRequest. Instead, developers relied upon the construction of COM objects via ActiveXObject("Msxml2.XMLHTTP") and ActiveXObject("Microsoft.XMLHTTP"). By the release of Internet Explorer 7 (2006), universal support for the XMLHttpRequest identifier had been achieved across all major browser engines. The XMLHttpRequest identifier has since become the established, cross-browser standard, embraced by Mozilla's Gecko rendering engine (2002), Apple's Safari browser versions (2004 onwards), and Opera (2005).

=== Standardization Efforts === The World Wide Web Consortium (W3C) issued the initial Working Draft specification for the XMLHttpRequest object on April 5, 2006. A subsequent Level 2 specification, introducing enhancements such as progress monitoring events, cross-origin request facilitation, and byte stream handling, was published by the W3C on February 25, 2008. By the close of 2011, the features defined in the Level 2 draft were successfully integrated back into the primary specification document. As of late 2012, the responsibility for maintaining this specification shifted to the WHATWG, where it continues development as a living document defined using Web IDL.

== Operational Usage Pattern == Generally, invoking an HTTP request using XMLHttpRequest adheres to a sequence of distinct programming stages.

Instantiation of the XMLHttpRequest object via its constructor call: Invocation of the open method to delineate the request method (GET, POST, etc.), specify the target resource URI, and elect between synchronous or asynchronous execution context: For asynchronous operational modes, the assignment of an event handler function designed to react to changes in the request's state: Initiation of the outbound request transmission by calling the send method, optionally supplying payload data: Processing of state transitions within the assigned event listener. Upon successful server acknowledgment and data return, the response content is typically accessible via the responseText property. The request transitions to state 4, signifying completion ('done'), when all processing ceases. Beyond these core procedural steps, XMLHttpRequest offers extensive options to govern request transmission characteristics and response parsing strategies. Custom header fields can be programmatically injected to influence server behavior. Furthermore, data payloads can be transmitted to the server as an argument to the send call. Response data can be automatically deserialized from JSON into native JavaScript objects, or streamed and processed incrementally rather than waiting for the total content to accumulate. Requests retain the capability to be unilaterally terminated or configured to automatically fail if a defined time limit is exceeded.

== Cross-Origin Resource Sharing (CORS) ==

Early in the development timeline of the World Wide Web, security restrictions were identified that limited the ability to invoke resources hosted on different domains than the originating document, leading to the need for solutions to manage cross-domain data exchange.